Update app.py

app.py

@@ -1,115 +1,119 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os
-from langchain_community.embeddings import HuggingFaceEmbeddings  # Using Hugging Face embeddings
-from langchain.vectorstores import FAISS
-from langchain_groq import ChatGroq
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-os.getenv("GROQ_API_KEY")
-
-def get_pdf_text(pdf_docs):
-    """Extracts text from uploaded PDF files."""
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-def get_text_chunks(text):
-    """Splits extracted text into manageable chunks."""
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-def get_vector_store(text_chunks):
-    """Creates and saves a FAISS vector store from text chunks."""
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # Using Hugging Face embeddings
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-def get_conversational_chain():
-    """Sets up a conversational chain using Groq LLM."""
-    prompt_template = """
-    Answer the question as detailed as possible from the provided context. If the answer is not in
-    the provided context, just say, "answer is not available in the context." Do not provide incorrect answers.
-
-    Context:
-    {context}?
-
-    Question:
-    {question}
-
-    Answer:
-    """
-    
-    model = ChatGroq(
-        temperature=0.3,
-        model_name="deepseek-r1-distill-llama-70b",  # Using Mixtral model through Groq
-        groq_api_key=os.getenv("GROQ_API_KEY")
-    )
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-    return chain
-
-def user_input(user_question):
-    """Handles user queries by retrieving answers from the vector store."""
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # Using Hugging Face embeddings
-
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    response = chain(
-        {"input_documents": docs, "question": user_question},
-        return_only_outputs=True
-    )
-
-    st.markdown(f"### Reply:\n{response['output_text']}")
-
-def main():
-    """Main function to run the Streamlit app."""
-    st.set_page_config(page_title="Chat PDF", page_icon=":books:", layout="wide")
-    st.title("Chat with PDF using DeepSeek Ai")
-    
-    st.sidebar.header("Upload & Process PDF Files")
-    st.sidebar.markdown(
-        "Using DeepSeek R1 model for advanced conversational capabilities.")
-    
-    with st.sidebar:
-        pdf_docs = st.file_uploader(
-            "Upload your PDF files:",
-            accept_multiple_files=True,
-            type=["pdf"]
-        )
-        if st.button("Submit & Process"):
-            with st.spinner("Processing your files..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("PDFs processed and indexed successfully!")
-
-    st.markdown(
-        "### Ask Questions from Your PDF Files :mag:\n"
-        "Once you upload and process your PDFs, type your questions below."
-    )
-
-    user_question = st.text_input("Enter your question:", placeholder="What do you want to know?")
-
-    if user_question:
-        with st.spinner("Fetching your answer..."):
-            user_input(user_question)
-
-    st.sidebar.info(
-        "**Note:** This app uses DeepSeek R1 model for answering questions accurately."
-    )
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_community.embeddings import HuggingFaceEmbeddings  # Using Hugging Face embeddings
+from langchain.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+os.getenv("GROQ_API_KEY")
+
+def get_pdf_text(pdf_docs):
+    """Extracts text from uploaded PDF files."""
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_text_chunks(text):
+    """Splits extracted text into manageable chunks."""
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    """Creates and saves a FAISS vector store from text chunks."""
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # Using Hugging Face embeddings
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    """Sets up a conversational chain using Groq LLM."""
+    prompt_template = """
+    Answer the question as detailed as possible from the provided context. If the answer is not in
+    the provided context, just say, "answer is not available in the context." Do not provide incorrect answers.
+
+    Context:
+    {context}?
+
+    Question:
+    {question}
+
+    Answer:
+    """
+
+    model = ChatGroq(
+        temperature=0.3,
+        model_name="deepseek-r1-distill-llama-70b",  # Using Mixtral model through Groq
+        groq_api_key=os.getenv("GROQ_API_KEY")
+    )
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+def user_input(user_question):
+    """Handles user queries by retrieving answers from the vector store."""
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")  # Using Hugging Face embeddings
+
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+
+    # Display the model's thought process
+    with st.expander("Model Thought Process"):
+        st.markdown("<think>Thinking...</think>", unsafe_allow_html=True)
+
+    st.markdown(f"### Reply:\n{response['output_text']}")
+
+def main():
+    """Main function to run the Streamlit app."""
+    st.set_page_config(page_title="Chat PDF", page_icon=":books:", layout="wide")
+    st.title("Chat with PDF using DeepSeek Ai")
+
+    st.sidebar.header("Upload & Process PDF Files")
+    st.sidebar.markdown(
+        "Using DeepSeek R1 model for advanced conversational capabilities.")
+
+    with st.sidebar:
+        pdf_docs = st.file_uploader(
+            "Upload your PDF files:",
+            accept_multiple_files=True,
+            type=["pdf"]
+        )
+        if st.button("Submit & Process"):
+            with st.spinner("Processing your files..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("PDFs processed and indexed successfully!")
+
+    st.markdown(
+        "### Ask Questions from Your PDF Files :mag:\n"
+        "Once you upload and process your PDFs, type your questions below."
+    )
+
+    user_question = st.text_input("Enter your question:", placeholder="What do you want to know?")
+
+    if user_question:
+        with st.spinner("Fetching your answer..."):
+            user_input(user_question)
+
+    st.sidebar.info(
+        "**Note:** This app uses DeepSeek R1 model for answering questions accurately."
+    )
+
+if __name__ == "__main__":
+    main()